Framework for developing algorithmic fairness

E3S web of conferences, 2023

In recent years, research into and concern over algorithmic fairness and bias in machine learning systems has grown significantly. It is vital to make sure that these systems are fair, impartial, and do not support discrimination or social injustices since machine learning algorithms are becoming more and more prevalent in decision-making processes across a variety of disciplines. This abstract gives a general explanation of the idea of algorithmic fairness, the difficulties posed by bias in machine learning systems, and different solutions to these problems. Algorithmic bias and fairness in machine learning systems are crucial issues in this regard that demand the attention of academics, practitioners, and policymakers. Building fair and unbiased machine learning systems that uphold equality and prevent discrimination requires addressing biases in training data, creating fairnessaware algorithms, encouraging transparency and interpretability, and encouraging diversity and inclusivity.

Amicus Curiae, 2019

This article discusses conceptions of fairness in algorithmic decision-making, within the context of the UK’s legal system. Using practical operational examples of algorithmic tools, itargues that such practices involve inherent technical trade-offs over multiple, competing notions of fairness, which are further exacerbated by policy choices made by those public authorities who use them. This raises major concerns regarding the ability of such choices to affect legal issues in decision-making, and transform legal protections, without adequate legal oversight, or a clear legal framework. This is not to say that the law does not have the capacity to regulate and ensure fairness, but that a more expansive idea of its function is required.

Business & Information Systems Engineering, 2023

Journal of Business Ethics, 2021

Recent advances in machine learning methods have created opportunities to eliminate unfairness from algorithmic decision making. Multiple computational techniques (i.e., algorithmic fairness criteria) have arisen out of this work. Yet, urgent questions remain about the perceived fairness of these criteria and in which situations organizations should use them. In this paper, we seek to gain insight into these questions by exploring fairness perceptions of five algorithmic criteria. We focus on two key dimensions of fairness evaluations: distributive fairness and procedural fairness. We shed light on variation in the potential for different algorithmic criteria to facilitate distributive fairness. Subsequently, we discuss procedural fairness and provide a framework for understanding how algorithmic criteria relate to essential aspects of this construct, which helps to identify when a specific criterion is suitable. From a practical standpoint, we encourage organizations to recognize that managing fairness in machine learning systems is complex, and that adopting a blind or one-size-fits-all mentality toward algorithmic criteria will surely damage people's attitudes and trust in automated technology. Instead, firms should carefully consider the subtle yet significant differences between these technical solutions.

2021

Machine Learning (ML) based predictive systems are increasingly used to support decisions with a critical impact on individuals' lives such as college admission, job hiring, child custody, criminal risk assessment, etc. As a result, fairness emerged as an important requirement to guarantee that ML predictive systems do not discriminate against specific individuals or entire sub-populations, in particular, minorities. Given the inherent subjectivity of viewing the concept of fairness, several notions of fairness have been introduced in the literature. This paper is a survey of fairness notions that, unlike other surveys in the literature, addresses the question of "which notion of fairness is most suited to a given real-world scenario and why?". Our attempt to answer this question consists in (1) identifying the set of fairness-related characteristics of the real-world scenario at hand, (2) analyzing the behavior of each fairness notion, and then (3) fitting these two e...

ArXiv, 2021

Algorithmic decision-making (ADM) increasingly shapes people’s daily lives. Given that such autonomous systems can cause severe harm to individuals and social groups, fairness concerns have arisen. A human-centric approach demanded by scholars and policymakers requires taking people’s fairness perceptions into account when designing and implementing ADM. We provide a comprehensive, systematic literature review synthesizing the existing empirical insights on perceptions of algorithmic fairness from 39 empirical studies spanning multiple domains and scientific disciplines. Through thorough coding, we systemize the current empirical literature along four dimensions: (1) algorithmic predictors, (2) human predictors, (3) comparative effects (human decision-making vs. algorithmic decision-making), and (4) consequences of ADM. While we identify much heterogeneity around the theoretical concepts and empirical measurements of algorithmic fairness, the insights come almost exclusively from We...

Equity and Access in Algorithms, Mechanisms, and Optimization, 2021

As machine learning (ML) is deployed in high-stakes domains, such as disease diagnosis or prison sentencing, questions of fairness have become an area of concern in its development. This interest has produced a variety of statistical fairness definitions derived from classical performance metrics which further expand the decisions that ML practitioners must make in building a system. The need to choose between these definitions raises questions about what conditions influence people to perceive an algorithm as fair or not. Recent results highlight the heavily contextual nature of fairness perceptions, and the specific conditions under which psychological principles such as framing can reliably sway these perceptions. Additional interdisciplinary insights include lessons from the replication crisis within psychology, from which we can glean best-practices for reproducible empirical research. We survey key research at the intersection of ML and psychology, focusing on psychological mechanisms underlying fairness preferences. We conclude by stating the continued need for interdisciplinary research, and underscore best-practices that can inform the state-of-the-art practice. We consider this research to be of a descriptive nature, enabling a deeper understanding and a substantiated discussion.

Minds and Machines

The problem of algorithmic fairness is typically framed as the problem of finding a unique formal criterion that guarantees that a given algorithmic decision-making procedure is morally permissible. In this paper, I argue that this is conceptually misguided and that we should replace the problem with two sub-problems. If we examine how most state-of-the-art machine learning systems work, we notice that there are two distinct stages in the decision-making process. First, a prediction of a relevant property is made. Secondly, a decision is taken based (at least partly) on this prediction. These two stages have different aims: the prediction is aimed at accuracy, while the decision is aimed at allocating a given good in a way that maximizes some context-relative utility measure. Correspondingly, two different fairness issues can arise. First, predictions could be biased in discriminatory ways. This means that the predictions contain systematic errors for a specific group of individuals...

2020

As machine learning (ML) is deployed in high-stakes domains, such as disease diagnosis or prison sentencing, questions of fairness have become an area of concern in the development of ML. This interest has produced a variety of statistical fairness definitions derived from classical performance metrics which further expand the decisions that ML practitioners must make in building a system. The need to choose between these definitions raises questions about what conditions influence people to perceive an algorithm as fair or not. Recent results highlight the heavily contextual nature of fairness perceptions, and the specific conditions under which psychological principles such as framing can reliably sway these perceptions. Additional interdisciplinary insights include lessons from the replication crisis within psychology, from which we can glean best-practices for reproducible empirical research. We survey key research at the intersection of ML and psychology, focusing on psychologi...

2021

Testing machine learning software for ethical bias has become a pressing current concern. In response, recent research has proposed a plethora of new fairness metrics, for example, the dozens of fairness metrics in the IBM AIF360 toolkit. This raises the question: How can any fairness tool satisfy such a diverse range of goals? While we cannot completely simplify the task of fairness testing, we can certainly reduce the problem. This paper shows that many of those fairness metrics effectively measure the same thing. Based on experiments using seven real-world datasets, we find that (a) 26 classification metrics can be clustered into seven groups, and (b) four dataset metrics can be clustered into three groups. Further, each reduced set may actually predict different things. Hence, it is no longer necessary (or even possible) to satisfy all fairness metrics. In summary, to simplify the fairness testing problem, we recommend the following steps: (1) determine what type of fairness is ...